{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54e3f2f7-3188-45e0-9424-9f0a9a68a0c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 步骤\n",
    "# 1、读取numpy文件\n",
    "# 2、onehot独热编码\n",
    "# 3、分为tain和test数据\n",
    "# 4、搭建CNN模型\n",
    "# 5、训练模型\n",
    "# 6、保存模型\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2258ec5-1703-4d9a-b552-a04d9f6c6f01",
   "metadata": {},
   "source": [
    "## 1、读取NPA文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dfdbe6e7-d8eb-4dd8-9d8e-8fc7764706e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入相关包\n",
    "import numpy as np \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3b02878-6a3a-4b0b-82d0-757c0c9efccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.load('./data/imageData.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "449f42e8-d9d6-4698-986e-c2ed3f1792a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1.yes', '1.yes', '1.yes', ..., '3.nose', '3.nose', '3.nose'],\n",
       "      dtype='<U6')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr['arr_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10c8161e-a7fc-45ae-8cfc-529e2e5b43a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_list = arr['arr_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f20ec404-4634-4adb-9146-f2fcbc91eb9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_list = arr['arr_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c283b89-5c7c-40d8-8a42-a0abdf8e1469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5328, 100, 100, 3), (5328,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_list.shape,label_list.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5edda16-5c2c-4b96-a622-4a96e69ae84d",
   "metadata": {},
   "source": [
    "## 2、onehot独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41d60d13-ab09-47e3-b7eb-5c6fdb076155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1.yes', '2.no', '3.nose'], dtype='<U6')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(label_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bea14a2a-4bca-4671-95c1-6cde092a88f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f8a6d2b-f9ad-4e2e-9125-131d59c8299c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实例化\n",
    "onehot =  OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ba72f87-f9d6-4246-a150-c90c510300d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 编码\n",
    "y_onehot = onehot.fit_transform(label_list.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5689c049-79b3-4696-aa2c-e0d4d1582501",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_onehot_arr = y_onehot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a79c8c48-f626-41b1-a812-1d88be7689ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 1.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_onehot_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9397ed-8690-4d85-a823-eded0960c57f",
   "metadata": {},
   "source": [
    "## 3、分为tain和test数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c3e22855-ad40-4347-9d1d-8793eb615a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4e2e30a-e684-4d4c-8ed5-5c6b445104fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_tain,y_test = train_test_split(img_list,y_onehot_arr,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0fcf874f-9740-4d2a-a34f-7c590694f580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4262, 100, 100, 3), (1066, 100, 100, 3), (4262, 3), (1066, 3))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape,x_test.shape,y_tain.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7db0861a-9d61-4312-ae03-b24519db6483",
   "metadata": {},
   "source": [
    "## 4、搭建CNN模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82dd5753-3d9e-4e96-bb03-3922732354ab",
   "metadata": {},
   "source": [
    "![](./cnn.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4961488b-0eb6-4e70-9b2c-53d0d6919b92",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9942720d-6b89-46e5-b2e8-65961d8be4a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow .keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a6b0adf9-46a5-4a6e-9381-b4cd0e091f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "adb9967c-5bb4-4e66-a22b-09a8abe3b62b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    layers.Conv2D(16,3,padding='same',input_shape=(100,100,3),activation='relu'),\n",
    "    layers.MaxPool2D(),\n",
    "    layers.Conv2D(32,3,padding='same',activation='relu'),\n",
    "    layers.MaxPool2D(),\n",
    "    layers.Conv2D(64,3,padding='same',activation='relu'),\n",
    "    layers.MaxPool2D(),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(166,activation='relu'),\n",
    "    layers.Dense(22,activation='relu'),\n",
    "    layers.Dense(3,activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2286c436-56c3-4f0b-8b58-1acf885b2958",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 编译模型\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),loss=tf.keras.losses.categorical_crossentropy,metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e2589813-933b-474c-ab32-9d18aa6476b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 100, 100, 16)      448       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 50, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 50, 50, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 25, 25, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 166)               1530022   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 22)                3674      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 69        \n",
      "=================================================================\n",
      "Total params: 1,557,349\n",
      "Trainable params: 1,557,349\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 预览模型\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c445367-b33c-46bc-9a30-2d99197c3cbe",
   "metadata": {},
   "source": [
    "## 5、训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "72469e4a-ebd3-46df-8a2e-168c4c72521e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "143/143 [==============================] - 6s 32ms/step - loss: 0.3254 - accuracy: 0.8757 - val_loss: 0.1241 - val_accuracy: 0.9587\n",
      "Epoch 2/10\n",
      "143/143 [==============================] - 2s 15ms/step - loss: 0.1023 - accuracy: 0.9686 - val_loss: 0.1026 - val_accuracy: 0.9719\n",
      "Epoch 3/10\n",
      "143/143 [==============================] - 2s 16ms/step - loss: 0.1010 - accuracy: 0.9667 - val_loss: 0.0752 - val_accuracy: 0.9747\n",
      "Epoch 4/10\n",
      "143/143 [==============================] - 2s 16ms/step - loss: 0.0622 - accuracy: 0.9814 - val_loss: 0.1189 - val_accuracy: 0.9634\n",
      "Epoch 5/10\n",
      "143/143 [==============================] - 2s 16ms/step - loss: 0.0399 - accuracy: 0.9891 - val_loss: 0.0672 - val_accuracy: 0.9812\n",
      "Epoch 6/10\n",
      "143/143 [==============================] - 2s 16ms/step - loss: 0.0142 - accuracy: 0.9956 - val_loss: 0.0894 - val_accuracy: 0.9784\n",
      "Epoch 7/10\n",
      "143/143 [==============================] - 2s 16ms/step - loss: 0.0232 - accuracy: 0.9933 - val_loss: 0.0779 - val_accuracy: 0.9794\n",
      "Epoch 8/10\n",
      "143/143 [==============================] - 2s 16ms/step - loss: 0.0171 - accuracy: 0.9952 - val_loss: 0.0842 - val_accuracy: 0.9794\n",
      "Epoch 9/10\n",
      "143/143 [==============================] - 2s 16ms/step - loss: 0.0119 - accuracy: 0.9965 - val_loss: 0.0950 - val_accuracy: 0.9803\n",
      "Epoch 10/10\n",
      "143/143 [==============================] - 2s 16ms/step - loss: 0.0035 - accuracy: 0.9992 - val_loss: 0.0889 - val_accuracy: 0.9869\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=x_train,y=y_tain,validation_data=(x_test,y_test),batch_size=30,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4f6bb3e8-43c0-4d17-a88e-63e2728c0a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看训练效果\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8d48ab0c-cb3c-4acb-b37a-1aa2f9acb1b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "history_pd = pd.DataFrame(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f55f6658-d639-4003-8a77-a7f2010719c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.193270</td>\n",
       "      <td>0.934069</td>\n",
       "      <td>0.124076</td>\n",
       "      <td>0.958724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.091632</td>\n",
       "      <td>0.971844</td>\n",
       "      <td>0.102583</td>\n",
       "      <td>0.971857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.087157</td>\n",
       "      <td>0.972079</td>\n",
       "      <td>0.075211</td>\n",
       "      <td>0.974672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049634</td>\n",
       "      <td>0.984280</td>\n",
       "      <td>0.118870</td>\n",
       "      <td>0.963415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.032795</td>\n",
       "      <td>0.991319</td>\n",
       "      <td>0.067218</td>\n",
       "      <td>0.981238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.016716</td>\n",
       "      <td>0.995307</td>\n",
       "      <td>0.089436</td>\n",
       "      <td>0.978424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.022719</td>\n",
       "      <td>0.992726</td>\n",
       "      <td>0.077937</td>\n",
       "      <td>0.979362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.018047</td>\n",
       "      <td>0.994134</td>\n",
       "      <td>0.084211</td>\n",
       "      <td>0.979362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.995542</td>\n",
       "      <td>0.095034</td>\n",
       "      <td>0.980300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.003957</td>\n",
       "      <td>0.999061</td>\n",
       "      <td>0.088862</td>\n",
       "      <td>0.986867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  0.193270  0.934069  0.124076      0.958724\n",
       "1  0.091632  0.971844  0.102583      0.971857\n",
       "2  0.087157  0.972079  0.075211      0.974672\n",
       "3  0.049634  0.984280  0.118870      0.963415\n",
       "4  0.032795  0.991319  0.067218      0.981238\n",
       "5  0.016716  0.995307  0.089436      0.978424\n",
       "6  0.022719  0.992726  0.077937      0.979362\n",
       "7  0.018047  0.994134  0.084211      0.979362\n",
       "8  0.012812  0.995542  0.095034      0.980300\n",
       "9  0.003957  0.999061  0.088862      0.986867"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "142d3dca-db83-45d5-8d03-001181913fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看损失\n",
    "plt.plot(history_pd['loss'])\n",
    "plt.plot(history_pd['val_loss'])\n",
    "plt.title('Model_loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend(['train set','test_set'],loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ad42de65-fff4-40d5-9910-eff7d899dd0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看准确率\n",
    "plt.plot(history_pd['accuracy'])\n",
    "plt.plot(history_pd['val_accuracy'])\n",
    "plt.title('Model_accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend(['train set','test_set'],loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "256cb311-d3eb-4f5b-9b0d-d112e3fb7615",
   "metadata": {},
   "source": [
    "## 6、保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6d02a5a7-8b4c-41cc-a328-2cd64a75a8ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('./data/face_mask_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e13e6748-1165-4d18-ace7-d6b4230de93e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
